Materials Science
Efficient Large-Scale STEM-EELS Simulations With Torched-TACAW
Martin Osmera, João Vaz, Paul M. Zeiger et al. (+1 more)
The time auto-correlation of auxiliary wave functions (TACAW) method enables efficient simulations of ultra-low-loss electron energy loss spectra (EELS) arising from vibrational and
magnon excitations. In practical applications to realistic materials systems, however, TACAW calculations become challenging due to the large system sizes required for models containing defects, interfaces,
impurities, or grain boundaries, as well as the substantial computational cost and data throughput associated with molecular dynamics and multislice calculations. Here we discuss a practical methodology for large-scale TACAW simulations and present torched-TACAW, a freely available implementation of the TACAW part of the described workflow for efficient STEM-EELS simulations. The overall approach combines molecular dynamics based on foundational machine-learned interatomic potentials, partitioning of elongated supercells, and on-the-fly processing of multislice outputs in order to enable near ab initio quality simulations with tractable
memory use and data flow. Using rutile TiO2 as a model system, we analyze important numerical aspects of the method, including windowing and supercell partitioning, and demonstrate atomic-resolution STEM-EELS simulations for thick samples.
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All authors Martin Osmera, João Vaz, Paul M. Zeiger, Ján Rusz
High Energy Physics (Theory)
Black Holes and Random Variables
Eric Perlmutter
We formulate an avatar of the Fyodorov-Hiary-Keating
conjecture for black hole microstate counts in
quantum gravity. By holography, this implies sharp bounds on interval counts of high-dimension primary operators in conformal field theory. The extremal fluctuations of these counts are characterized by a random variable, with a prescribed tail distribution. At large $N$, these order-one erratic fluctuations set a quantitative limit on the resolution of the semiclassical AdS gravitational path integral. Gaussian random models for state counts arise naturally in this context; we express the phenomenon of erratic $N$-dependence in AdS/CFT as a decorrelation property of these models. Our broader point is to suggest that AdS black hole microstate spectra and their field theory duals should exhibit the extreme value statistics of random
matrices, lying in the universality class of Gaussian log-correlated fields.
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High Energy Physics (Phenomenology)
Sideband Structure of Axion Electrodynamics
Run-Min Yao, Xiao-Jun Bi, Peng-Fei Yin et al. (+1 more)
We develop a Floquet–Bloch sideband formulation of the linearized Maxwell–
axion system in a coherent periodic
axion background. Linearizing around prescribed
magnetic and
axion fields, we show that the pump generates a sideband ladder of
photon and
axion branches. Near an isolated folded degeneracy, this ladder reduces to a two-mode crossing whose
algebra is fixed by the
symplectic signatures of the colliding modes. In temporal fixed-momentum
evolution, same-Krein-sign collisions give stable avoided crossings, whereas opposite-sign collisions give parametric instabilities, unifying the
axion-
photon difference channel with the Mathieu and Masaki-Aoki-Soda resonances. In stationary fixed-frequency transfer, the corresponding flux signatures distinguish bounded forward conversion from forward-backward stop bands and distributed reflection. Ray projection of a temporal pump gives a related but local WKB description of driven forward mixing, with an effective wavenumber distinct from the true
axion momentum. External-field diagrams reproduce the sideband selection rules, and full temporal monodromy calculations verify the instability
topology and finite-coupling shifts.
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All authors Run-Min Yao, Xiao-Jun Bi, Peng-Fei Yin, Qing-Guo Huang
High Energy Astrophysical Phenomena
Timing and spectral analysis of the 2025 outburst of 4U 1630$-$47 with NICER
Haifan Zhu, Mariano Méndez, Xiao Chen et al. (+1 more)
We analyzed NICER observations of the 2025 outburst of the black hole X-ray binary 4U~1630$-$47 to investigate the spectral–timing properties of its transient low-frequency quasi-periodic oscillations (QPOs) and millihertz-scale quasi-regular modulation (QRM). During the rising phase of the outburst, the QPO centroid frequency increased from $\sim 0.24$ Hz to $\sim 3.43$ Hz. Wavelet-based state separation shows that the with-QPO intervals are associated with a higher inner disk temperature and a lower diskbb normalization than the without-QPO intervals, while the
photon index ($\Gamma$) shows weaker changes within the uncertainties. Near the outburst peak, the source displayed a weak QRM at $\sim 0.07$ Hz with a fractional rms amplitude of $\sim 4.7\%$, lower than that of the heartbeat state observed in 2023. Phase-resolved
Hilbert–Huang analysis shows that the inner disk temperature is positively correlated with the X-ray flux, the diskbb normalization is anticorrelated, and $\Gamma$ varies only weakly. Overall, the short-timescale spectral–timing variability is expressed most clearly through the disk-related parameters. The transient QPOs are therefore consistent with short-timescale disk-related variability during the rising phase, whereas the millihertz-scale QRM may represent a weaker heartbeat-like variability mode appearing near the outburst peak.
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All authors Haifan Zhu, Mariano Méndez, Xiao Chen, Wei Wang
High Energy Physics (Experiment)
Separating charm and bottom decay electrons with point-cloud deep learning on the charged-hadron environment
Jingyu Zhang, Wanbing He and Long Ma
Electrons from semi-leptonic decays of charm ($D$) and bottom ($B$)
hadrons are important probes in high-energy collisions, while their separation remains challenging due to the similarity of the underlying decay
topologies. In this work, the charged-
hadron environment associated with each electron is represented as an unordered point cloud and combined with the electron kinematics and charge. Several set-based machine learning architectures, including DeepSets,
Transformer, and graph neural networks, are used to distinguish electrons from
prompt $D$ and direct $B$ decays without explicit vertex observables. The three architectures achieve comparable performance. Moreover, the
Transformer is found to perform better than a boosted decision tree trained on physics-motivated high-level observables. At an efficiency of about 40%, the classifier reaches a purity level close to 80% on the test sample. The classifier response is strongly related to the
geometric and topological properties of the charged-
hadron environment. The information learned by the classifier is further studied using non-
prompt $D$ electrons and charged
hadrons from different angular regions. Our results indicate that the learned separation is driven mainly by the later stages of the decay chain rather than by initial production effects. The angular-region study further shows that useful discriminating information extends beyond the near- and away-side cones. This method is also evaluated for mid-rapidity
proton–
proton collisions at both RHIC and LHC energies.
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Materials Science
Ultrafast Demagnetization Governed by Spin Fluctuations in CaRuO$_{3}$/SrTiO$_{3}$ Superlattice
Yu-Han Gao, Wen-Xiao Shi, Yuan-Sha Chen et al. (+10 more)
For ultrafast magnetization switching devices, critical slowing down in conventional ferromagnets near their Curie temperature constitutes a key challenge that must be overcome. In contrast to this typical behavior, we observe an anomalous acceleration of demagnetization in CaRuO$_{3}$/SrTiO$_{3}$ superlattices, a moderately correlated weak itinerant ferromagnet. The demagnetization rate increases with rising temperature, pump fluence, and applied
magnetic field. To explain these anomalous phenomena, we develop a phenomenological model integrating the three-temperature model with self-consistent renormalization theory. Because the intrinsic gradient magnetism of the superlattice suppresses the typical divergence of specific heat, the conventional thermodynamic bottleneck is bypassed. Our model reveals that this decoupling enables the ultrafast dynamics to be predominantly governed by the
spin-fluctuation-driven enhancement of the electron-
spin scattering vertex. Our work demonstrates how spatial inhomogeneity can decouple macroscopic thermodynamic singularities from microscopic scattering processes, offering a new paradigm for manipulating ultrafast
spin dynamics in correlated
quantum materials. The pronounced sensitivity of the demagnetization rate to external parameters further suggests the potential for designing highly tunable ultrafast spintronic devices that leverage enhanced fluctuations near the
magnetic instability.
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All authors Yu-Han Gao, Wen-Xiao Shi, Yuan-Sha Chen, Ji-Rong Sun, Qing-Lin Yang, Xu Yang, Zhuo Deng, Peng-Tao Yang, Zheng Chang, Hong-Mei Feng, Wei He, Xiang-Qun Zhang, Zhao-Hua Cheng
Instrumentation and Methods for Astrophysics
HARMONI at ELT: line spread functions in a diffraction limited spectrometer
Stephen P. Todd, Éamonn J. Harvey, Anna MacIver et al. (+8 more)
HARMONI is the first light, adaptive optics assisted, near-IR integral field spectrograph for the ELT. It covers a spectral range from 800~nm to 2450~nm with resolving powers from 3000 to 7000 and spatial sampling of 25~mas and 6~mas. It can operate in two adaptive optics modes - SCAO (including a high contrast capability) and MCAO. The project is resuming its final design phase after a rescope design phase in 2025. Diffraction of the pupil becomes significant in a spectrograph where the slit width is comparable to the diffraction limited PSF. When the spatial coherence due to the narrow slit is considered, the resulting line spread function can be narrower than the
geometric width of the input slit, with a non-linear dependence on the size of the pupil aperture after the slit. We outline the impact of these diffraction and spatial filtering effects on the line spread function of HARMONI and identify parameters that should be considered when designing a diffraction limited spectrograph.
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All authors Stephen P. Todd, Éamonn J. Harvey, Anna MacIver, William Taylor, Eduard Muslimov, Kjetil Dohlen, Matthias Tecza, Magali Loupias, Paula Bañares-Palacios, Mark Swinbank, Ryan Griffiths
Statistical Mechanics
Alternative routes to universal diversity scaling in component systems: from proteomes to large language models
Andrea Mazzolini, Leonardo Agasso, Filippo Valle et al. (+3 more)
Remarkably common statistical laws characterize the diversity scaling and its fluctuations across a wide range of complex "component systems". These regularities are often interpreted as signatures of an underlying innovation mechanism driving the growth of component diversity, but the basic ingredients necessary for their emergence remain poorly understood. In particular, from
language and technological artifacts to genomes and gene expression patterns, the number of distinct components grows sublinearly with system size, while its variance scales approximately as the square of its mean. This behavior is consistent across diverse systems, raising the question of whether general constraints or emergent principles underlying diversity and innovation define the architectures of realizations with different numbers of components. To address this question, we derive analytical conditions for the joint emergence of these two diversity laws within a broad class of growth models, showing that they require a specific asymptotic dependence of the innovation probability on diversity and system size. We then demonstrate that the same macroscopic laws arise in a different class of models with latent heterogeneity, where quadratic fluctuation scaling always emerges asymptotically as a consequence of general statistical principles, essentially the law of total variance, without explicitly assuming an innovation mechanism or any specific rule for system assembly. We compare these predictions with empirical data from
language, genomes, LEGO constructions, and texts generated by large
language models. Our results show that empirical diversity scaling laws strongly constrain generative models but do not uniquely identify the mechanisms generating diversity, revealing a close correspondence between innovation-driven growth models and latent-variable descriptions.
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All authors Andrea Mazzolini, Leonardo Agasso, Filippo Valle, Michele Caselle, Marco Cosentino Lagomarsino, Matteo Osella
Materials Science
Differentiable inverse design of short-range order in high-entropy alloys: from target sro to target property
Tiancheng Ding and Conrard Giresse Tetsassi Feugmo
Short-range order (SRO) governs the mechanical response of multi-principal-element
alloys, but designing an
alloy for a target property usually means solving two disconnected problems: building a structure matching a desired SRO pattern, then separately checking its property, with no shared optimization. This work replaces the standard random-swap search (reverse Monte Carlo) with a gradient-based approach: atom occupancy is treated as continuous rather than fixed, so the whole process can be tuned using gradient descent, the same method used to train neural networks. This builder matches random-swap accuracy on small systems, but is six times faster and eight times more accurate on large 4000-atom systems, and scales smoothly to
alloys with many elements without extra bookkeeping. A physics-based correction term, adapted from prior two-element work and extended here to many elements, keeps designed structures thermodynamically realistic rather than just numerically matching the target SRO pattern. A small neural network then predicts mechanical properties directly from composition and SRO statistics, closing the loop from target property back to structure. Tested on nine face-centered-cubic and body-centered-cubic
alloys, the pipeline captured SRO-driven stiffness changes from -20% to +57%, and
cell-size checks showed at least 864 atoms are needed to get the direction and size of these changes right, since the commonly used 108-atom
cells can mislead. Against real simulations for a cobalt-chromium-nickel
alloy, the method matched three of four target stiffness values within 6%. The method is released as an open-source Python package, anisro, offering a practical route to gradient-based, property-driven
alloy design.
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Statistical Mechanics
Exact amplitude relations for diffusion-limited aggregation
Thomas C Halsey
It has been known for several decades that the third moment of the multifractal spectrum of the harmonic measure for
diffusion-limited aggregates is linked to the underlying fractal dimension of the cluster. We demonstrate, using an argument based on the Hastings-Levitov formulation of
diffusion-limited aggregation (DLA) in two dimensions, an even stronger link, connecting the universal amplitude of the third moment to the cluster fractal dimension. This argument can be used for both the standard circular DLA as well as DLA in a cylinder (i.e., with periodic boundary conditions); in the latter case the relationship of the amplitude to the fractal dimension is weaker, and must be extracted via a scaling analysis.
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Atmospheric and Oceanic Physics
Midlatitude cloud radiative effects reinforce storm tracks on seasonal timescales
Or Hadas
Midlatitude storm tracks play a central role in Earth's
climate by transporting heat from low to high latitudes. Their strength is therefore set by the meridional temperature gradients, traditionally linked to differential
solar heating. This framework predicts a substantial seasonal reduction in storm activity, as meridional insolation gradients vanish during summer. Yet in the Southern Hemisphere, storm activity decreases by only about 40% from its seasonal maximum. Here, we show that cloud radiative effects contribute to the seasonal maintenance of storm activity by reinforcing the temperature gradients that sustain it. Satellite observations reveal that sunlight reflected by midlatitude clouds in early summer creates a substantial meridional gradient in surface heating, despite the nearly uniform summer insolation. Idealized aquaplanet simulations then show that shortwave cloud radiative effects reinforce meridional sea-surface temperature gradients, thereby strengthening storm activity primarily during late summer and autumn, while longwave cloud effects partly offset this response. To interpret these results, we develop a simple theoretical model linking storms, clouds, and sea-surface temperature gradients. The model captures the seasonal response and demonstrates the importance of two emergent cloud properties: the maximum attainable cloud albedo and the sensitivity of cloud cover to storm activity. Together, these findings indicate that cloud radiative feedbacks help maintain the thermal gradients that sustain storm activity on seasonal timescales. More broadly, they reveal a strong coupling among storms, clouds, and the ocean spanning distinct spatial and temporal scales.
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Optics
Revealing Sharp Spectral Features with Complex Frequency Excitations: Challenges and Opportunities
Jacob B Khurgin, Andrea Alu, Vitaly Kozlov et al. (+1 more)
Broadening of spectral and spatial responses due to intrinsic loss in real materials often hides sharp features. One recently recognized route to recover those features is to probe the system with complex-frequency (CF) signals that decay exponentially in time: a suitably tailored temporal decay can compensate for loss and reveal an intrinsic, narrow response. However, generating rapidly decaying optical waveforms in real time is often challenging (the required decay times may be in the range of tens of femtoseconds). A recently proposed alternative synthesizes the CF response numerically after detection of conventional, real-frequency signals using
Fourier post-processing. Here we explore advantages and challenges of these approaches: we show that a physical CF excitation robustly sharpens spectral features in the presence of noise, while a post-detection synthesized CF response shows only limited improvement once realistic detection and readout noise is considered. At the same time, in low-noise conditions a much simpler post-detection filtering procedure attains equal or better recovery than the synthesized CF reconstruction, making the synthesis unnecessary in practice.
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All authors Jacob B Khurgin, Andrea Alu, Vitaly Kozlov, Philippe Lalanne
Cellular Automata and Lattice Gases
The Binary Crisis Clock: Controlled by Sparse Ternary Interventions
Małgorzata Nowak-Kȩpczyk
We investigate modular
Laplacian automata on triangular
lattices with
evolution governed by binary and ternary
moduli. Extending previous studies on square
lattices, we examine how
lattice geometry influences long-term growth, density, fragmentation, and the emergence of self-similar structures. We further investigate whether sparse ternary interventions can stabilize predominantly binary dynamics. The experiments reveal that mask geometry is the primary determinant of large-scale morphology. Full hexagonal masks generate recurrent density crises and fragmentation, whereas triangular masks support persistent growth and reveal a threshold phenomenon governed by growth-capable
nuclei. Although seed
symmetry influences transient behaviour, the asymptotic morphology is inherited mainly from the mask. To control binary fragmentation, we investigate sparse developmental ternary perturbations in which a small number of carefully timed occurrences of modulus 3 are inserted into an otherwise binary sequence. A Monte Carlo optimization demonstrates that as few as three interventions are sufficient to redirect the subsequent binary
evolution toward substantially denser carpet-like configurations. The effectiveness of this strategy depends primarily on the timing of the interventions rather than on their number. Analysis of the post-intervention dynamics shows that ternary shaping does not replace binary
evolution. Instead, it produces denser self-similar structures, substantially reduces crisis depth, and resets the phase of the binary crisis clock. The results suggest that geometry determines the family of admissible morphologies, whereas sparse developmental perturbations select favourable long-term trajectories within that family.
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Fluid Dynamics · Cross-listed from cs.LG
Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
Mojgan Alishiri and Amirhossein Arzani
Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces. However, their neural network-based architectures make them opaque models, obscuring the
reasoning behind their predictions. In this work, we introduce a self-explainable operator learning framework that overcomes this challenge by reformulating operator learning as a linear combination of generalized functional linear models expressed through integral equations. Exploiting the additive decomposability of these integral equations, we divide the input domain into subdomains and compute localized integrals to evaluate the contribution of each region to the final prediction. This decomposition enables direct interpretability where the model explains both inputs and outputs by linking specific input regions to corresponding output patterns, thereby revealing which spatial features drive predictions. We demonstrate the framework on function-to-scalar and function-to-function mappings in fluid flow problems involving blood flow and unsteady aerodynamics. The results show that the operator most often prioritizes regions with strong feature gradients, providing physically meaningful insight into the model's decision-making process. Comparisons with established post-hoc explainability methods demonstrate qualitative agreement while highlighting the key advantage of the proposed approach: explainability is embedded directly within the operator structure itself and does not require an external tool. Therefore, our framework provides a mathematically transparent and physically interpretable approach to uncover relationships within data, fostering trust in machine learning for scientific applications by enabling more informed data-driven analysis of physical systems.
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Nuclear Experiment
Gamma-ray production cross sections in proton interactions with natMg, natSi and 56Fe targets: measurement over the energy range of $E_p = 66$-125 MeV, data analysis, results and discussion. Astrophysical implications
M. Debabi, S. Ouichaoui, A. Belhout et al. (+33 more)
We have measured
nuclear gamma-ray line production cross sections in interactions of highly accelerated
proton beams with various target
nuclei abundant in astrophysical sites. The experiments were carried out at the 200-MV Separated Sector Cyclotron (SSC) of iThemba LABS (near Cape Town, in South Africa) using a high-energy resolution and high efficiency detection system for registering the emitted gamma-ray
photons. We report and discuss in this paper the collected experimental data sets for various gamma-ray lines produced in bombarding natMg, natSi and 56Fe targets with
proton beams of incident energies of Ep = 66, 80, 95, 110 and 125 MeV. After describing the experimental set up and the data analysis method used, we report and discuss our total experimental cross section results in comparisons to previous counterparts from the literature, to a semi-empirical compilation and to the predictions of
nuclear reaction theory via performed TALYS code calculations. Significantly improved agreements between theory and experiment are point out when using our modified optical model potential and Bêta (lambda) level deformation parameters instead of the default input parameters built in TALYS. Finally, we put into perspective the applications of our results in
nuclear physics and astrophysics with drawing relevant conclusions. gammaKeywords:
Proton-induced
nuclear reactions; gamma-ray production cross sections; gamma-ray spectrometry; gamma-ray spectroscopy; Astrophysical implications
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All authors M. Debabi, S. Ouichaoui, A. Belhout, J. Kiener, E. A. Lawrie, J. J. Lawrie, D. Moussa, W. Yahia-Cherif, Y. Rahma, H. Benhabiles-Mezhoud, A. Trabelsi, T. D. Bucher, A. Chafa, L. J. Conradie, S. Damache, I. Deloncle, J. L.Easton, C. Hamadache, F. Hammache, P. Jones, V. B. Kheswa, N. A. Khumalo, A. Kunyana, T. Lamula, S. N. T. Majola, J. Ndayishimye, D. Negi, S. P. Noncolela, S.Ouziane, P. Papka, N. de Séréville, J. F. Sharpey-Schafer, O. Shirinda, V. Tatischeff, M. Wiedeking, S. Wyngaardt
Medical Physics
Free-breathing Pulmonary Relaxometry at 0.55T
Pavlos Panos, Oliver Bieri, Maurice Pradella et al. (+2 more)
Purpose: To evaluate the feasibility of an integrated, free-breathing workflow for automated 2D pulmonary relaxometry (T1, T2) at 0.55T. Methods: A 2D inversion recovery ultra-fast balanced steady-state free precession (IR-uf-bSSFP) sequence was adapted to achieve high-temporal sampling of the transient phase at 0.55T. The technique was validated in a phantom and tested in eight healthy volunteers as well as one patient. A fully automated pipeline was developed, featuring multi-contrast registration for motion correction and deep learning based lung
segmentation to enable voxel-wise nonlinear fitting for T1 and T2 map generation. Results: Phantom results were in close agreement with reference scans. In-vivo, the proposed free-breathing framework effectively mitigated respiratory motion, yielding quantitative maps in close agreement with breath-hold references. Healthy lung parenchyma relaxation times were T1 = (930+-40)ms and T2 = (90+-8)ms. In a patient case, the method successfully distinguished a solid lung mass from healthy parenchyma, with the lesion showing elevated T1 (960ms vs 810ms in the surrounding parenchyma). Conclusions: Simultaneous free-breathing T1 and T2 mapping of the lung is feasible at 0.55T using a fully automated pipeline. By eliminating breath-holds and external gating, this approach improves patient compliance and potentially facilitates the use of quantitative lung MRI in routine clinical practice.
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All authors Pavlos Panos, Oliver Bieri, Maurice Pradella, Katrin E. Hostettler, Grzegorz Bauman
Computational Physics · Cross-listed from cs.LG
An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks
Joseph Webb, Sadok Jerad and Coralia Cartis
Physics-informed neural networks (PINNs) have emerged as a promising route to solve partial differential equations, yet they have struggled to reach the precision of classical solvers. The obstacle is increasingly understood to be one of optimisation, owing to the severely ill-conditioned loss landscape. We present $\textbf{DSGNAR}$: Doubly-Sketched Gauss-Newton with Adaptive Ratio, a scalable second-order optimisation framework that confronts this ill-conditioning and, in doing so, obtains unprecedented accuracy and speed. $\textbf{DSGNAR}$ couples a doubly-sketched Gauss-Newton model with a novel strategy that carefully controls both regularisation and step length. Across a suite of problems spanning nonlinear, chaotic, multi-scale, high-dimensional, and Navier-Stokes, the framework greatly improves on the state of the art: able to attain relative $\ell_2$ errors as low as $3\times10^{-16}$ in double precision, improve contemporary results by five orders of magnitude on the canonical Burgers' equation, and as much as eight orders on a high-dimensional Poisson problem, while remaining markedly faster. We further show that, in single precision, solutions at the limit of round-off error can be obtained very quickly: Burgers' equation to $\ell_2^{\text{rel}} = 4.75 \times 10^{-7}$ in under ten seconds. The framework is also robust to the choice of architecture, arithmetic precision, and initial hyperparameters. The code is available at https://www.github.com/wephy/physics-informed-neural-networks
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Solar and Stellar Astrophysics
Discovery of sulfur oxides in the ejecta of a B[e] supergiant
C. Bordiu, J. Ricardo Rizzo, D. Navarro-Almaida et al. (+12 more)
B[e] supergiants represent a rare class of luminous, evolved massive
stars surrounded by dusty circumstellar disks. Since their intense UV fields were long thought to sterilize their surroundings, molecular detections beyond carbon monoxide have remained elusive, leaving their chemical reservoirs largely unexplored. Whether these environments can sustain a complex molecular chemistry is a fundamental question with significant astrochemical implications. Here we report the detection of chemically rich molecular gas surrounding the B[e] supergiant HD~87643, using ALMA observations. Our data reveal the presence of the sulfur
oxides SO and SO$_2$ and other sulfur-bearing species, marking the first detection of these molecules in an early-type evolved massive
star. We find a high fractional abundance of SO$_2$ relative to H$_2$, which our chemical modelling can reproduce in timescales as short as $\sim$10$^4$ yr in an oxygen-rich environment. These results indicate that the detected molecules trace a short-lived, rapidly evolving phase of out-of-
equilibrium chemistry. Furthermore, we measure an anomalously low $^{32}$SO/$^{33}$SO, that we attribute to mass-independent fractionation driven by intense photochemistry. This mechanism mirrors processes proposed to explain the $^{33}$S excesses in the atmosphere of the Archaean Earth. Our findings suggest that B[e] supergiants could serve as unique laboratories for studying sulfur chemistry under extreme radiation conditions, opening potential avenues to investigate the fractionation processes that shaped the isotopic signatures found in the early geological record.
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All authors C. Bordiu, J. Ricardo Rizzo, D. Navarro-Almaida, A. Fuente, F. Bufano, G. Umana, S. Loru, A. C. Ruggeri, C. Buemi, F. Cavallaro, L. Cerrigone, A. Ingallinera, P. Leto, S. Riggi, C. Trigilio
Mesoscale and Nanoscale Physics
Entropy of Non-Abelian Anyons from Slow Quasiparticle Dynamics in Quantum Hall Interferometers
Eran Sela and Mitali Banerjee
Non-Abelian anyons emerging in fractional
quantum Hall states carry a characteristic
entropy, $\Delta S = k_B \log d$, where $d$ is the anyon's
quantum dimension. This $\mathcal{O}(1)$
entropy can, in principle, be extracted from charge measurements of an antidot via Maxwell relations. However,
equilibrium charge measurements in fractional antidots have proven to be challenging with conventional charge detectors. Here, we propose a scheme based on an antidot embedded in an interferometer, in which the charge can be inferred from the recently observed time-dependent switching of the interference phase. Performing such non-local charge measurements at
equilibrium, the characteristic $\mathcal{O}(1)$
entropy of non-Abelian anyons (e.g., $d = \sqrt{2}$ for the $\nu = 5/2$ state) can be extracted for intermediate temperatures, which exceed the level spacing of the interferometer edge, but are much smaller than the level spacing of the antidot.
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Nuclear Experiment
$l$-forbidden $\mathbf{M1}$ strengths near $^{100}$Sn from knockout reactions in Cd and Sn
T. J. Gray, K. L. Jones, R. Grzywacz et al. (+21 more)
Neutron knockout reactions on beams of $^{104,102}$Cd, and $^{104}$Sn are presented. States in the residual $^{103,101}$Cd and $^{103}$Sn
nuclei are populated, including low-lying $7/2^+$ states of $\nu g_{7/2}$ character. These states have half-lives $\approx 400$ ps due to their low energy and hindered $B(M1; 7/2^+ \rightarrow 5/2^+)$ strengths. The excited-state half-lives were measured using their Doppler-shifted lineshapes, and the resulting $B(M1)$ strengths are compared to Valence Space In Medium Similarity Renormalization Group (VS-IMSRG) calculations. The VS-IMSRG calculations under-predict the $l$-forbidden $M1$ strengths in the $^{100}$Sn region, as well as in other regions of the
nuclear chart near $^{40}$Ca and $^{208}$Pb.
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All authors T. J. Gray, K. L. Jones, R. Grzywacz, B. A. Brown, A. Gade, B. C. He, T. Miyagi, A. Peter, M. J. Basson, T. Beck, C. M. Campbell, G. Cerizza, J. Chung-Jung, I. Cox, P. Farris, R. Ghimire, S. Gillespie, M. Grinder, A. Hill, S. D. Pain, A. Palmisano-Kyle, K. P. Rykaczewski, D. Weisshaar, M. Williams
Strongly Correlated Electrons
A density matrix renormalization group approach to quantum point contacts
Nair Aucar Boidi and Mikhail Kiselev
Using the density
matrix renormalization group (DMRG) combined with the correction-vector method, we investigate the competition between an harmonic potential and repulsive interactions in a one-dimensional fermionic system. The
parabolic confinement induces spatial inhomogeneity, and by tuning its
curvature one can continuously interpolate between a potential well–relevant for cold-atom setups–and a
quantum barrier, as realized in mesoscopic systems such as
quantum point contacts. We analyze how the ground-state particle distribution evolves with the strength and sign of the confining potential and how the confinement reshapes the spectral weight of the local density of states (LDOS) at the center of the chain. In the barrier regime, a localized peak emerges in the electron part of the spectrum ($\omega >0$) as a direct consequence of the potential. In contrast, in the well configuration and for weak interactions, a localized feature persists but shifts to the hole sector ($\omega <0$). However, for stronger interactions, the LDOS no longer displays clear signatures of the external potential, indicating that correlations dominate over single-particle confinement.
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Materials Science
Coherent two-dimensional electronic spectroscopy integrated with confocal back focal plane microscopy
Trideep Kawde, Pavel Trofimov, Anton Trenczek et al. (+3 more)
We introduce a setup for coherent two-dimensional electronic spectroscopy in the pump-probe reflection geometry that is integrated with a confocal back focal plane imaging microscope. The angle-resolved capability is utilized to control pump and probe wavevectors, while real space imaging enables co-localization of the collection spots for linear and ultrafast experiments. Compression of pulses down to 20 fs is achieved. We demonstrate the capabilities of this approach on an exfoliated WSe$_2$ monolayer on Si/SiO$_2$. The setup is suited to investigate
excitons and
exciton-polaritons in 2D Materials and their
heterostructures.
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All authors Trideep Kawde, Pavel Trofimov, Anton Trenczek, Matteo Russo, Jasper Wilhelm Schwering, Hélène Seiler
High Energy Physics (Phenomenology)
Searching for single production of a vector-like $Y$ quark decaying into $bW$ at the FCC-eh
Liangliang Shang, Weifan Zhu, Bingfang Yang et al. (+1 more)
We investigate the exclusion and discovery potential for single production of a vector-like $Y$
quark with electric charge $Q=-4/3$, followed by the decay $Y\to bW$, at the FCC-eh. The $Y$
quark is allowed to couple to both first- and third-generation down-type
quarks. The analysis is performed for an electron-beam polarization of $P_e=-80\%$ at $\sqrt{s}=3.46$, $5.29$, and $6.9~\mathrm{TeV}$. Both leptonic and hadronic $W$-
boson decay channels are considered. In the hadronic channel, the boosted $W$-
boson is reconstructed as a $W$-jet, and kinematic observables are used to suppress the Standard Model (SM) backgrounds. By performing a detailed detector simulations and event analysis, we present the $2\sigma$ exclusion limits and $5\sigma$ discovery reaches in the $g^*$–$m_Y$ plane, where $g^*$ is $Y$ coupling strength to the SM
quarks. We find that the hadronic channel can provide stronger exclusion and discovery sensitivities, which are improved with increasing $\sqrt{s}$ at the FCC-eh.
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All authors Liangliang Shang, Weifan Zhu, Bingfang Yang, Stefano Moretti
Disordered Systems and Neural Networks · Cross-listed from math.PR
Fluctuations of the Sherrington-Kirkpatrick free energy at critical temperature
Hang Du and Brice Huang
We consider the Sherrington-Kirkpatrick
spin glass model at the critical inverse temperature $\beta = 1$ with zero external field. We prove that the free energy $F_N = F_{N,\beta=1}$ of this model has variance \[ \mathrm{Var}(F_N) = \frac16 \log N + O(1)\,, \] confirming a physics prediction of Aspelmeier \cite{aspelmeier2008free}, and that the centered and scaled $F_N$ satisfies a Gaussian CLT. We also identify the critical two-replica overlap scale, proving \[ \mathbb{E} \langle R_{1,2}^2\rangle \asymp N^{-2/3}\,, \] as conjectured by Talagrand \cite{talagrand2011mean2}, together with a uniform exponential moment bound for $N^{1/3} |R_{1,2}|$. The key input is a critical reweighted moment method, in the spirit of the “small subgraph conditioning” technique from probabilistic combinatorics, but capable of capturing diverging fluctuations. Through this reweighting, we relate the critical SK model to the BBP critical edge, which determines the overlap and fluctuation scales.
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Statistical Mechanics
Theory of collective learning in populations of adaptive agents
Gerhard Jung, Johann Asnacios, Misaki Ozawa et al. (+2 more)
We investigate homogeneous populations of smart active
agents that exchange information with their neighbors to perform a decentralized learning process aimed at achieving a prescribed macroscopic state. Such
agents may, for example, represent simple microrobots. The exchanged information comprises tunable parameters governing the
agent dynamics, referred to as the individual policy, together with an internal
memory encoding previously visited states. This
memory is used to evaluate a reward that quantifies the success of a policy to achieve the prescribed state. We extend the kinetic-theory description of collective learning in spatially homogeneous systems [Phys. Rev. Lett. 134, 248302 (2025)] and derive formal
evolution equations for the distribution of policies across the population. A central outcome of our theory is the emergence of an effective reward function that fully determines the
evolution of the policy distribution and encapsulates the microscopic details of the
agents physical and
memory dynamics. We obtain closed equations for the policy mean and variance which admit explicit time-dependent solutions under the assumption of Gaussian-distributed
memories and polices. To illustrate the framework, we present a series of minimal microscopic models, considering both perfect and partial separation of physical,
memory and policy exchange time scales, as well as models with one- and two-dimensional policies. The obtained theoretical results compare well with
agent-based numerical simulations. The theory captures key aspects of collective learning, including the influence of population diversity and reward fluctuations on learning performance. Finally, we discuss potential applications to swarm robotics and machine learning, and highlight connections with classical models of biological
evolution, including the Replicator equation and the Moran model.
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All authors Gerhard Jung, Johann Asnacios, Misaki Ozawa, Olivier Dauchot, Eric Bertin
Quantum Physics · Cross-listed from cs.IT
Generalized Extended Codes with Applications in Entanglement-Assisted Qubit and Qutrit Codes
Yang Li, Martianus Frederic Ezerman, Shitao Li et al. (+2 more)
We prove that any generalized extended code is monomially equivalent to the Hermitian dual of a code which is closely related to a second kind of extended code of $\C^{\perp_{\rm H}}$. Every $[n+1,k+1]_{q^2}$ linear code $\D$ with $d(\D^{\perp_{\rm H}})>1$ is monomially equivalent to the generalized extended code $\C({\bf u},a)$ of an $[n,k]_{q^2}$ linear code $\C$ for a fixed $a\in\F_{q^2}^{*}$ and some ${\bf u}\in\F_{q^2}^{n}$. We then characterize the Hermitian hull and Hermitian dual distance of $\C({\bf u},a)$ in terms of the position of ${\bf u}$ relative to $\C+\C^{\perp_{\rm H}}$ and the interaction between ${\bf u}$ and the minimum weight codewords of $\C^{\perp_{\rm H}}$, respectively. We obtain explicit criteria to independently control the expected Hermitian hull dimension and Hermitian dual distance of $\C({\bf u},a)$. In particular, several conditions for simultaneously increasing the Hermitian hull dimension and the Hermitian dual distance of $\C({\bf u},a)$ are derived. Applying these results to the Hermitian construction for EAQECCs gives us $267$ new EA
qubit codes of lengths $n \leq 40$ and $14$ new EA qutrit codes of lengths $n \leq 25$ compared to the best-known codes in Grassl's code tables and the imporvements recorded in very recent works in the literature. Among the new parameter sets, we confirm improvements for $236$
qubit and $8$ qutrit codes.
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All authors Yang Li, Martianus Frederic Ezerman, Shitao Li, San Ling, Zhonghua Sun
Quantum Physics
Phase-Space Representations of Quantum Error-Correcting Codes
Enrico Bozzetto and Jonte R. Hance
We connect the structure
theorem for quasiprobability representations to
quantum error correction. For a code space invariant under a subgroup of an extended Weyl-Heisenberg group, the Brif-Mann construction gives a semi-functorial representation whenever its kernel satisfies the Stratonovich-Weyl axioms, and composes on channels covering full error-correction cycles. With phase-space parity available, a kernel other than the ordinary Wigner one exists iff the code space is invariant under a
lattice of displacements: among compact-phase-space bosonic codes, this is only the ideal Gottesman-Kitaev-Preskill family; in finite dimension, this is every qudit stabiliser code, whose negative volume for odd
prime dimension is the syndrome-averaged logical magic. For rotation-symmetric, one-
photon, non-Pauli qudit and
spin codes only the ordinary kernel remains, its negativity reflecting the physical carrier, not logical content; a sector-graded representation built from recovery isometries restores a resource reading. We add a channel atlas, a closed-form magic lifetime under displacement noise, a Kirkwood-Dirac layer, and finite-energy corrections.
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Astrophysics of Galaxies
Resolved HI and Environmental Dynamics
M. Ramatsoku, P. Serra, N. Deg et al. (+8 more)
Spatially resolved, deep HI observations from SKA precursors and pathfinders such as MeerKAT, FAST, and ASKAP have demonstrated their ability to reveal the complex interactions between
galaxies and their environments. These include, but are not limited to, recent observations of the Virgo cluster showing that the hydrodynamical effects of ram pressure stripping can operate effectively at unexpectedly large cluster-centric distances. In the Fornax cluster, the discovery of long HI tails with mixed
tidal-ram-pressure origins indicates the interplay between gravitational and hydrodynamical mechanisms. Similar HI features in nearby filaments and
galaxy groups, where ram pressure is expected to be weak, highlight the influence of hydrodynamical processes even in low-density environments. Multi-resolution studies have further revealed signs of cold gas
accretion and HI replenishment driven by
tidal interactions. While highly informative, these studies remain limited to small, specific regions of the sky. With SKA-mid AA4, it will become possible to carry out deep, spatially resolved HI imaging over hundreds of square degrees, covering environments from isolated
galaxies to filaments. By reaching column-density sensitivities between $1.0 \times 10^{18}$ and $\sim 1.0 \times 10^{19}~\mathrm{cm^{-2}}$ at physical resolutions of $\sim$10 and $\sim$1 - 2 kpc, respectively, and by enabling sensitive, contiguous observations of wide areas within short integrations, SKA-mid AA4 will allow the construction of large, statistically representative samples of
galaxies and detailed studies of environmental mechanisms operating across the full range of these less-studied environments at resolved scales.
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All authors M. Ramatsoku, P. Serra, N. Deg, R. Ianjamasimanana, A. Sorgho, G. De Lucia, K. Spekkens, L. Verdes-Montenegro, H. Yoon, B. Namumba, M. Meyer
Atmospheric and Oceanic Physics
Mathematical Exploration of Earth Gravitational Field Impact on Seasonal Wind Flux in a Tropical Region
M. S. Adiaha, V. O. Chude, E. E. Oku et al. (+4 more)
The Earth's gravitational field exerts a significant influence on atmospheric dynamics, including the behavior of seasonal wind flux, defined by periodic variations in wind speed and direction. While temperature gradients and Earth's rotation are established drivers of wind patterns, the role of gravitational forces in modulating these processes remains poorly understood. This study investigates the mathematical relationship between gravitational variations and seasonal wind flux in Nigeria, a region of pronounced climatic variability and varied wind patterns. Utilizing Navier-Stokes equations for atmospheric dynamics,
Fourier decomposition for seasonal wind flux analysis, and Pearson correlation coefficients for gravitational-wind interactions, we analyze 2010-2020 meteorological data, alongside gravitational field measurements from the GRACE (
Gravity Recovery and
Climate Experiment) satellite. Results show significant annual fluctuations in average wind speed (5.1-5.6 m/s) and gravitational variations (9.60-9.95 mGal), with an inverse relationship observed in certain years, suggesting a coupling between atmospheric dynamics and gravitational forces. Seasonal wind flux exhibits a distinct sinusoidal pattern, peaking mid-year and declining toward year-end, consistent with Nigeria's monsoon
climate. Correlation coefficients between gravitational variations and wind flux range from 0.79 to 0.87, indicating a strong positive relationship. These findings underscore the importance of gravitational forces in modulating wind patterns and highlight the potential for integrating gravitational data into
climate models, thereby enhancing accuracy of weather
forecasting and renewable energy planning. This study provides a foundational framework for further exploration of gravitational influences on atmospheric processes, with implications for global
climate science and sustainable energy strategies.
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All authors M. S. Adiaha, V. O. Chude, E. E. Oku, G. I. Nwaka, O. J. Abimbola, I. K. Rajendra, P. O. Abam
Quantum Physics
Thermodynamics of Quantum Reservoir Computing
Lixiang Ding and Xingze Qiu
Quantum reservoir computing provides a framework for processing complex temporal data, yet its fundamental computational and energetic limits remain unresolved. Here, we establish a non-
equilibrium thermodynamic framework that links the macroscopic predictive performance of driven open
quantum systems to their microscopic energetic costs. By mapping Holevo capacities onto the Bogoliubov-Kubo-Mori
geometric manifold, we analytically prove that the computational peak within the
quantum critical region originates from a spectral resonance: the closing of the intrinsic energy gap forces the reservoir's internal transition frequencies to align with the chaotic drive. To evaluate the associated thermodynamic costs, we introduce
quantum informational dissipation to quantify the non-predictive historical data retained by the reservoir. This allows us to derive a generalized Landauer bound for continuous temporal processing, which reveals a fundamental thermodynamic trade-off: the critical resonance that maximizes predictive capacity simultaneously maximizes informational dissipation and the irreversible work required for environmental erasure. Furthermore, coherence decomposition demonstrates that
quantum coherences amplify predictive capacity without demanding additional mechanical work. These findings establish the fundamental energetic limits of
quantum learning devices, providing theoretical principles for designing energy-efficient
quantum neuromorphic hardware.
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